Conventionally, information technology (hereinafter “IT”) organizations consolidate physical servers into a smaller set of physical servers running many virtual servers. In this virtual server environment, most or all hardware resources, such as memory, central processing unit (CPU), storage and network are shared among the virtual servers. Many organizations are reducing the number of physical servers through virtualization technologies which allow for multiple virtual servers to run on one or more physical servers. With consolidation of servers it is inevitable that capacity bottlenecks will develop in sharing or resources such as CPU, RAM, and Storage. That is, if the shared resources are over-utilized, users can experience performance degradation and even downtime.
Conventional approaches for determining capacity bottlenecks is very labor intensive, requiring system administrators to manually examine numerous capacity graphs to determine where bottlenecks exist. That is, using conventional capacity reporting software is extremely time consuming and requires examination of hundreds of charts. For example, in a small environment with only 50 ESX hosts, a systems administrator would have to study nearly 260 graphs to evaluate utilization of just four resources: (50 Hosts+5 clusters+10 Resource Pools)*4 Resource types=260 graphs.
Furthermore, conventional techniques do not provide any means for proactively managing and allocating shared resources in a virtual environment. For example, conventional approaches do not anticipate resource allocation or future utilization that may lead to bottlenecks in CPU, memory, storage and disk Input/Output (hereinafter “I/O”), which can lead to performance problems and costly downtime situations. Likewise, conventional systems do not provide means for dealing with over-allocation of resources which can drive up the cost per virtual machine and diminishing returns on the investment in virtualization.